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Python 3.x 保存熊猫数据帧的取消勾选字典抛出AttributeError:';数据帧';对象没有属性'_数据';_Python 3.x_Pandas_Dataframe_Pickle - Fatal编程技术网

Python 3.x 保存熊猫数据帧的取消勾选字典抛出AttributeError:';数据帧';对象没有属性'_数据';

Python 3.x 保存熊猫数据帧的取消勾选字典抛出AttributeError:';数据帧';对象没有属性'_数据';,python-3.x,pandas,dataframe,pickle,Python 3.x,Pandas,Dataframe,Pickle,我有一个类,它执行分析并将结果(即数据帧)附加为对象属性: >>> print(test.image.locate_DF) y x mass ... raw_mass ep frame 0 60.177142 59.788709 33.433414 ... 242.080256 NaN 0 1 60.651991 59.773904 33.7243

我有一个类,它执行分析并将结果(即数据帧)附加为对象属性:

>>> print(test.image.locate_DF)
              y          x       mass  ...    raw_mass        ep  frame
0     60.177142  59.788709  33.433414  ...  242.080256       NaN      0
1     60.651991  59.773904  33.724308  ...  242.355784       NaN      1
2     60.790437  60.190234  31.117164  ...  236.276671       NaN      2
3     60.771933  60.048123  33.558372  ...  240.981395       NaN      3
4     60.251282  59.775139  31.881009  ...  239.239022       NaN      4
...         ...        ...        ...  ...         ...       ...    ...
7212  68.186380  76.477449  18.122817  ...  176.523091       NaN   9410
7213  68.764444  76.574091  17.486454  ...  173.448306       NaN   9415
7214  68.191152  76.473477  17.402975  ...  172.848119  0.868326   9429
7215  67.034103  76.025885  17.010951  ...  170.928067 -0.600854   9431
7216  68.583276  75.309592  17.852992  ...  178.271558       NaN   9432
随后,我将所有重要的对象属性保存在一个字典中,并对其进行pickle以供以后使用:

def save_parameters(self, filepath):
        
        param_dict = {}

    try:
            self.image.locate_DF
        except AttributeError:
            pass
        else:
            param_dict['optical_locate_DF'] = self.image.locate_DF

    with open(filepath, 'wb') as handle:
            pickle.dump(param_dict, handle, 5)
在尝试加载该pickle文件时,我一点问题都没有,数据帧可以完美地加载:

>>> test.save_parameters('test.pickle')
>>> with open('test.pickle', 'rb') as handle:
...     result = pickle.load(handle)
...
>>> print(result.keys())
dict_keys(['optical_path', 'optical_feature_diameter', 'optical_feature_minmass', 'optical_locate_DF', 'electrical_path', 'electrical_raw_data', 'electrical_processed_data', 'electrical_mean_voltage'])
>>> print(result['optical_locate_DF'])
              y          x       mass  ...    raw_mass        ep  frame
0     60.177142  59.788709  33.433414  ...  242.080256       NaN      0
1     60.651991  59.773904  33.724308  ...  242.355784       NaN      1
2     60.790437  60.190234  31.117164  ...  236.276671       NaN      2
3     60.771933  60.048123  33.558372  ...  240.981395       NaN      3
4     60.251282  59.775139  31.881009  ...  239.239022       NaN      4
...         ...        ...        ...  ...         ...       ...    ...
7212  68.186380  76.477449  18.122817  ...  176.523091       NaN   9410
7213  68.764444  76.574091  17.486454  ...  173.448306       NaN   9415
7214  68.191152  76.473477  17.402975  ...  172.848119  0.868326   9429
7215  67.034103  76.025885  17.010951  ...  170.928067 -0.600854   9431
7216  68.583276  75.309592  17.852992  ...  178.271558       NaN   9432

[7217 rows x 9 columns]
但是,在hpc上对这些文件运行分析后,然后尝试打开同一个pickle文件(它现在的名称不同,但它是如上所示的同一个文件,并且对其执行了相同的分析),pandas抛出了一个属性错误。它声明dataframe没有“\u data”属性。字典具有相同的密钥,并且非数据帧的密钥打印时不会出现任何问题:

>>> resultfile = '../results/diam_15_minmass_17_dist_50_mem_5000_tracklength_500/R9_DNA_50mV_001.pickle'
>>> with open(resultfile, 'rb') as handle:
...     result = pickle.load(handle)
...
>>> print(result.keys())
dict_keys(['optical_path', 'optical_feature_diameter', 'optical_feature_minmass', 'optical_locate_DF', 'optical_tracking_distance', 'optical_tracking_memory', 'optical_tracking_DF', 'optical_kinetics_DF', 'electrical_path', 'electrical_raw_data', 'electrical_processed_data', 'electrical_mean_voltage'])
>>> print(result['optical_locate_DF'])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/core/frame.py", line 680, in __repr__
    self.to_string(
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/core/frame.py", line 801, in to_string
    formatter = fmt.DataFrameFormatter(
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/io/formats/format.py", line 593, in __init__
    self.max_rows_displayed = min(max_rows or len(self.frame), len(self.frame))
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/core/frame.py", line 1041, in __len__
    return len(self.index)
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/core/generic.py", line 5270, in __getattr__
    return object.__getattribute__(self, name)
  File "pandas/_libs/properties.pyx", line 63, in pandas._libs.properties.AxisProperty.__get__
  File "/Users/stevenvanuytsel/miniconda3/envs/simultaneous_measurements/lib/python3.8/site-packages/pandas/core/generic.py", line 5270, in __getattr__
    return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute '_data'
>>resultfile='../results/diam_15_minmass_17_dist_50_mem_5000_tracklength_500/R9_DNA_50mV_001.pickle'
>>>以open(resultfile,'rb')作为句柄:
...     结果=pickle.load(句柄)
...
>>>打印(result.keys())
记录键([‘光路’、‘光特征直径’、‘光特征最小质量’、‘光定位’、‘光跟踪距离’、‘光跟踪存储器’、‘光跟踪DF’、‘光动力学DF’、‘电路径’、‘电原始数据’、‘电处理数据’、‘电平均电压’]))
>>>打印(结果['optical\u locate\u DF'])
回溯(最近一次呼叫最后一次):
文件“”,第1行,在
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/core/frame.py”,第680行,in_u_repr__
self.to_字符串(
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/core/frame.py”,第801行,在to_字符串中
格式化程序=fmt.DataFrameFormatter(
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/io/formats/format.py”,第593行,in_uinit__
self.max\u rows\u displated=min(max\u rows或len(self.frame)、len(self.frame))
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/core/frame.py”,第1041行,in_u_len__
返回长度(自索引)
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/core/generic.py”,第5270行,位于__
返回对象。\uuuGetAttribute(self,name)
文件“pandas/_libs/properties.pyx”,第63行,在pandas.\u libs.properties.AxisProperty.\uu获取__
文件“/Users/stevenvanuytsel/miniconda3/envs/synchronous_measurements/lib/python3.8/site packages/pandas/core/generic.py”,第5270行,位于__
返回对象。\uuuGetAttribute(self,name)
AttributeError:“DataFrame”对象没有属性“\u data”

我查阅了pickle手册,并通过一系列的问题,但我似乎无法找出这里出了什么问题。有人知道如何解决这个问题,以及我是否仍然可以访问该数据吗?

经过漫长而痛苦的交叉检查模块版本后,我发现这个错误是由更新引起的在pandas版本中。我的mac仍然运行pandas 1.0.5,而hpc运行pandas 1.1.0。显然,两者之间存在不匹配(不确定是在酸洗之后还是用于保存的其他文件格式).

我也遇到了同样的问题。我在使用Pandas 1.1.1的环境中生成了Pandas数据帧,并将其保存到pickle文件中

with open('file.pkl', 'wb') as f:
    pickle.dump(data_frame_object, f)
在另一个会话中取消勾选并打印数据帧后,我得到了相同的错误。在不同环境中的一些测试显示了以下模式:

  • 熊猫环境>=1.1.0:工程
  • Pandas==1.0.5的环境:如上所述的错误消息
  • Pandas==1.0.3的环境:内核崩溃
我在使用HDF5格式时遇到了相同的错误,因此它似乎与dataframe和不同版本存在兼容性问题


在受影响的环境中将Pandas更新为1.1.1为我解决了问题。

也许问题已经解决。
嗯,但我还是想补充一些意见

我将pkl文件保存在服务器上,但当我在MAC上加载它时,它崩溃了,显示
'Dataframe'对象没有属性'\u data'


最后,我发现我的Mac电脑上的pandas是1.0.5,而服务器上的pandas是1.1.5。当我将其更新到最新版本时,它才起作用。

检查维度。是否都一样?检查是否有“\u数据”,如error所说。由于这与我刚才在计算机上执行的分析完全相同,我希望数据帧是相同的,具有相同的尺寸等。但是,我无法检查,因为我无法加载数据帧。与该数据帧交互的每个命令都会抛出相同的“\u data”错误。有没有其他方法可以检查?调用
result时t['optical_locate_DF'].值
,我得到一个递归错误(调用python对象时超出了最大递归深度)。